A Research Plan for GOES-R Risk Reduction



Renewal Proposal to

National Oceanic and Atmospheric Administration

National Environmental Satellite, Data, and Information Service

Office of Systems Development

Washington DC

Research and Development for GOES-R Risk Reduction for

Mesoscale Weather Analysis and Forecasting

Cooperative Institute for Research in the Atmosphere

Colorado State University

Foothills Campus

Fort Collins CO 80523-1375

Dr. Thomas H. Vonder Haar,

Principal Investigator

In partnership with

Dr. Mark DeMaria

Regional and Mesoscale Meteorology Team Leader

NESDIS Office of Research and Applications

Telephone: 970-491-8405

Email: Mark.DeMaria@

Debra Molenar

CIRA/Regional and Mesoscale Meteorology (RAMM) Team

Colorado State University

Telephone: 970-491-8447

Email: Molenar@cira.colostate.edu

March 2005

Period of Activity: July 1, 2005 – June 30, 2006

____________________________ __________________________

Dr. Thomas H. Vonder Haar, P.I. Anita Montgomery

Director, CIRA Research Administrator

(970) 491-8448 Office of Sponsored Programs

(970) 491-6586

1. Introduction

The next generation GOES satellites (beginning with GOES-R) will include an Advanced Baseline Imager (ABI) and Hyperspectral Environmental Suite (HES) with vastly improved spectral, spatial and temporal resolution relative to the current GOES I-M series satellites. The GOES-R era will begin early in the next decade, and will be part of a global observing system that includes polar orbiting satellites with comparable spatial and spectral resolution instrumentation. However, GOES-R will have superior temporal resolution. A continuing science study is proposed to use numerical simulations and existing in situ and satellite data to better understand the capabilities of these advanced instruments for mesoscale weather analysis and prediction. This science study will help to reduce the time needed to fully utilize GOES-R as soon as possible after launch.

The first phase of this study is a three-year project to simulate subsets of GOES-R observations existing satellites and to create synthetic GOES-R observations using numerical cloud model simulations coupled with radiative transfer code. A part of the modeling project is to develop an advanced data assimilation method based upon the ensemble Kalman filtering approach that can be used to assess the value of GOES-R data for numerical weather prediction. This new method is referred to as the Maximum Likelihood Ensemble Filter (MLEF). This proposal is for the third year of the first phase, which began in July of 2003, and a transition to phase 2, which is described below.

The second phase of the study will make use of the knowledge gained in the first phase to develop experimental products. The second phase will test these products when possible using existing operational and experimental satellites. Data assimilation experiments with real observations will also be performed in the second phase. This aspect of the project will be closely coordinated with the Joint Center for Satellite Data Assimilation (JCSDA). The second phase will also include a training and outreach component on both the national and international levels. This work will be coordinated with the Virtual Institute for Satellite Integration Training (VISIT) program, the International Virtual Laboratory efforts of the World Meteorological Organization and the Satellite Hydrology and Meteorology (SHyMet) Training Program.

In the third phase, preparation will begin on the development of Day-1 operational products, and training activities will increase. Increased coordination with the JCSDA will occur during phase three to identify methods to utilize GOES-R data in operational mesoscale numerical models.

One of the advantages of GOES-R is the high temporal resolution that is possible from geostationary orbit. The emphasis of our science study is on mesoscale atmospheric phenomena that evolve on time scales faster than that which can be sampled from polar orbiting satellites. These phenomena include tropical cyclones, severe weather and mesoscale aspects of winter weather, including lake-effect snowfall, as well as the detection of atmospheric hazards such as fog, dust and volcanic ash. This emphasis will continue throughout the project. References to previous results are provided in section 2, followed by plans for year 3 (the current proposal). A research and budget outline of the longer-term phases of the project is also provided, followed by a detailed budget for the current year.

2. Summary of Results from Year 1 and 2

The emphasis of the first two years was on the establishment of the case study database, and the set up of the numerical cloud model and radiative transfer code. Detailed quarterly reports were provided to NOAA which are available from



and



3. Current Year (FY05) Plans

In the third year of phase one, the case study data collection activities will continue. The radiative transfer code currently can generate synthetic imagery for 10 of the 16 channels proposed for the GOES-R Advanced Baseline Imager. Work will continue to provide generalize this code to include additional channels.

The study of the potential improvements in Derived Product Imagery (DPI) from GOES-R due to the increased spatial and temporal resolution will be written up for publication.

Very promising results of using hyperspectral soundings to monitor tropical cyclone environments and hurricane eyes were obtained in year 2. This work will be continued by obtaining additional AIRS data cases to confirm the eye sounding results from hurricane Isabel, and the environmental soundings from hurricanes Lili, Fabian and Isabel.

The development of the ensemble data assimilation methods will continue. The next step is to apply the MLEF technique to a more realistic situation where the simulated distribution is more similar to what will be available from GOES-R. The emphasis of this work will be on the information content of the GOES-R data that the MLEF technique provides.

A smart principal component imagery technique was developed to assist with optimal channel selection. This method will be applied to simulated and synthetic GOES-R data to begin development of prototype products for fog, dust and volcanic ash detection.

A cloud top structure climatology and related algorithm for severe weather nowcasting is under development using existing GOES data. This study will be continued by assessing the potential value of GOES-R data. The potential for using lightning data for this application will also be investigated.

GOES infrared data has been used for decades to estimate tropical cyclone intensity (the Dvorak method). A prototype algorithm for GOES-R will be developed from polar orbiting data with comparable spatial resolution.

A transition from case studies to real time data will begin. Initial development of a web site to display prototype products will begin.

Interaction with training programs (VISIT, SHyMet, WMO) will increase. Work will continue on the ABI synthetic imagery tool to illustrate the sensitivity of imagery to idealized inputs.

4. Longer Range Plans

As described in the Introduction, this project is part of a longer-range science plan for GOES-R Risk Reduction for mesoscale weather. Brief summaries and proposed budgets are provided below.

Phase 1: FY03-FY05:

Phase 1 is using a case study approach to simulate subsets of GOES-R observations with existing satellites, and to create synthetic GOES-R observations using numerical cloud model simulations coupled with radiative transfer code. The emphasis is to demonstrate the utility of GOES-R and begin development of prototype mesoscale nowcast and forecast products. Advanced data assimilation techniques are being developed to help assess the information content of satellite observations for mesoscale weather forecasting. Techniques such as principal component imagery are being applied to real and synthetic imagery for the mesoscale product development. The budget for phase 1 is summarized below. Coordination with on-going satellite training programs is being initiated.

Budget for Phase 1:

FY03 – 350 K (already funded)

FY04 – 197 K (already funded)

FY05 – 285 K (proposed)

Phase 2: FY06-FY09:

In phase 2, the results from the phase 1 case studies will be applied to experimental product development when possible using existing experimental and operational satellites. These products will be applied to similar mesoscale weather events as in the case studies, and experimental real-time results will be made available via the Internet. The numerical modeling studies will transition from the underlying CSU RAMS model to the Weather Research and Forecast (WRF) model, to become more aligned with the efforts at operational forecast centers. In addition, the emphasis of the assimilation experiments will shift from idealized experiments to those with real data. It is anticipated that the MLEF assimilation system will be in a mature stage as phase 2 progresses. A new aspect of phase 2 will be the increased emphasis on training material through coordination with the Virtual Institute for Satellite Integration and the International Virtual Laboratory in coordination with the WMO and the Regional Meteorological Training Centers. Training material will be developed and delivered, sample simulated data sets will be made available via the web, and experimental products using existing satellite data will be provided.

Proposed Budget for Phase 2:

FY06 – 350 K

FY07 - 400 K

FY08 - 400 K

FY09 - 400 K

Phase 3 – FY10-FY12

In the third phase, preparations will begin for testing experimental GOES-R mesoscale products based upon the results from phases 1 and 2. Education and training activities will increase in preparation for the launch of GOES-R. The numerical modeling aspects of the project will be even more closely coordinated with NCEP and other operational forecast centers.

Proposed Budget for Phase 3:

FY10 – 500 K

FY11 – 600 K

FY12 – 600 K

5. Project Personnel

Project Oversight will be provided by Prof. Thomas Vonder Haar (CIRA/CSU) and Dr. Mark DeMaria (NESDIS/ORA). Scientific guidance will also be provided by Dr. James Purdom, a CIRA Senior Research Scientist, and former Director of ORA.

The numerical modeling studies will primarily be performed by Drs. Dusanka Zupanski, Louis Grasso, M. Sengupta with oversight from Mark DeMaria. Dr. Dusanka is a CIRA employee who formerly worked for the NCEP Environmental Modeling Center, and is an expert on mesoscale modeling and data assimilation. Louis Grasso has extensive experience with the RAMS model, and was a former student of Dr. Bill Cotton, the original developer of RAMS. M. Sengupta will lead the radiative transfer modeling activities. M. DeMaria is a NESDIS ORA employee with an extensive background in tropical cyclone analysis and forecasting, and numerical weather prediction.

The database development will be overseen by Debra Molenar and M. DeMaria. Debra leads the RAMM Team computer infrastructure group and has considerable experience in database management, satellite data processing and programming. Dave Watson, Kevin Micke, and Hiro Gosden (CIRA research associates) will provide the computer support for the project, and Bernadette Connell will coordinate the training aspects. Kathy Fryer will provide administrative support.

Don Hillger will lead the information component analysis segment of the project, and is the primary focal point for coordination with ORA to obtain AIRS temperature and moisture retrievals.

The project also will include a graduate student to focus on a specific aspect of the research, depending upon her interests, and a student hourly employee to assist with data processing. The inclusion of students will help cultivate future scientists that have familiarity with the GOES-R program.

5. Proposed Budget

The proposed budget for the current year of the project is $285 K. A detailed breakdown of the costs is provided in the attached spreadsheet. Budget estimates for the out-years of this proposal are included in section 4.

6. Project Coordination and Documentation

This research is part of a larger GOES-R Risk Reduction program that is being coordinated with NESDIS/ORA and NESDIS/OSD. Quarterly progress reports will be provided to OSD and ORA management, and research results will be presented at annual activities reviews.

7. Budget Explanation

I. and II. PERSONNEL and FRINGE BENEFITS

Salaries and benefits are requested for the personnel that will be performing this research, and providing administrative and computer support. In a basis consistent with our long-standing Memorandum of Understanding between NOAA and Colorado State University, the enclosed budget specifically includes support for administrative and clerical personnel (Fryer) directly associated with the technical and managerial administration of GIMPAP.  This support is “quid pro quo” for the reduced indirect cost rate agreed upon in the long-standing subject memoranda. K. Fryer will provide communication and collaboration support, assist in the acquisition and distribution of reference materials relevant to the conception and execution of the project, technical editing of scientific manuscripts, specialized reports and conference papers. All other budgeted personnel are directly involved in the research which is identified in the Statement of Work (SOW). 

III. DOMESTIC TRAVEL

Funds are requested for two trips to Washington, DC for coordination with ORA scientists. These trips are required to obtain the necessary radiative transfer modeling capabilities, and training on some of the specialized experimental satellite data to be used in this research. Trips to scientific conferences are also requested for presentation of results.

IV. OTHER

1. The NT computer charges provide computer and data support associated with this project. The NT hourly rate is determined by CIRA and depends on the actual cost of the CIRA computer operations. CIRA charges Windows NT computer costs on an hourly basis, based on log-on time collected electronically via infrastructure programs.

2. Much of this research is performed in a McIDAS programming environment. A McIDAS Users Group (MUG) fee is necessary to use and update this software.

3. Funds are requested for publication of research results.

V. MATERIALS and SUPPLIES

The project requires has massive data storage requirements. Funds are requested to accommodate the collection of new case study data and to store new numerical model simulations.

VI. TUITION

It is anticipated that one graduate student will be supported as part of this research project.

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